https://github.com/GoodAI/goodai-ltm-benchmark
공식 코드Beyond Prompts: Dynamic Conversational Benchmarking of Large Language Models
Papers with Code 아카이브
Benchmarking
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Gorilla-Lab-SCUT/AffordanceNet
공식 코드3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
https://github.com/Gorilla-Lab-SCUT/TTAC2
공식 코드https://github.com/GradOpt/E3
공식 코드https://github.com/Grenzlinie/MatTools
공식 코드MatTools: Benchmarking Large Language Models for Materials Science Tools
https://github.com/HDCVLab/EDFace-Celeb-1M
공식 코드EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
https://github.com/HDCVLab/MC-Blur-Dataset
공식 코드https://github.com/HKBU-HPML/CovidNet3D
공식 코드https://github.com/HKUST-Aerial-Robotics/spatiotemporal_semantic_corridor
공식 코드Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor
https://github.com/HPAI-BSC/SuSy
공식 코드Present and Future Generalization of Synthetic Image Detectors
https://github.com/HannahKirk/Hatemoji
공식 코드https://github.com/HarrieO/OnlineLearningToRank
공식 코드https://github.com/HenryPengZou/ImplicitAVE
공식 코드https://github.com/HewlettPackard/separable-operator-networks
공식 코드https://github.com/HewlettPackard/trust-ml
공식 코드Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness
https://github.com/Hoangpham13579/end_to_end_anomaly_detection
공식 코드Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System
https://github.com/I2RDL2/ASTAR-3D
공식 코드A*3D Dataset: Towards Autonomous Driving in Challenging Environments
https://github.com/IAAR-Shanghai/UHGEval
공식 코드https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework
공식 코드Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
https://github.com/IBM-HRL-MLHLS/IBM-Causality-Benchmarking-Framework
공식 코드Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
https://github.com/IBM/AIF360
공식 코드https://github.com/IBM/IPC-graph-data
공식 코드IPC: A Benchmark Data Set for Learning with Graph-Structured Data
https://github.com/IBM/LLM-performance-prediction
공식 코드LLM-Pilot: Characterize and Optimize Performance of your LLM Inference Services
https://github.com/IBM/document2slides
공식 코드D2S: Document-to-Slide Generation Via Query-Based Text Summarization
https://github.com/IBM/helm-enterprise-benchmark
공식 코드https://github.com/IBM/itbench-sample-scenarios
공식 코드ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
https://github.com/IBM/itbench-sre-agent
공식 코드ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
https://github.com/IBM/terratorch
공식 코드https://github.com/ICLab4DL/GNNBenchEffectiveness
공식 코드Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
https://github.com/IDEA-Research/detrex
공식 코드https://github.com/IDSIA/crepo
공식 코드CREPO: An Open Repository to Benchmark Credal Network Algorithms
https://github.com/IDT-ITI/Adv-XAI-Deepfakes
공식 코드https://github.com/IMSY-DKFZ/probability-of-false-claims
공식 코드False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims
https://github.com/IOHprofiler/IOHAnalyzer
공식 코드IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
https://github.com/IOHprofiler/IOHalgorithm
공식 코드Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
https://github.com/IRVLUTD/SceneReplica
공식 코드SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.